{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42bf01fb",
   "metadata": {},
   "source": [
    "# We're going to build question and answer bot\n",
    "\n",
    "That allow you to search through youtube transcripts using natural language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48547ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --quiet openai tqdm ratelimiter retry datasets pylance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e570f4",
   "metadata": {},
   "source": [
    "## Download the data\n",
    "700 videos and 208619 sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a8987fcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset json (/Users/changshe/.cache/huggingface/datasets/jamescalam___json/jamescalam--youtube-transcriptions-08d889f6a5386b9b/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['title', 'published', 'url', 'video_id', 'channel_id', 'id', 'text', 'start', 'end'],\n",
       "    num_rows: 208619\n",
       "})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "data = load_dataset('jamescalam/youtube-transcriptions', split='train')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "893c60d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "700"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.to_pandas().title.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ac2b6a3",
   "metadata": {},
   "source": [
    "## Prepare context\n",
    "\n",
    "Create context of 20 sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "121a7087",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "window = 20\n",
    "stride = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "107ef00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def contextualize(raw_df, window, stride):\n",
    "    def process_video(vid):\n",
    "        # For each video, create the text rolling window\n",
    "        text = vid.text.values\n",
    "        time_end = vid[\"end\"].values\n",
    "        contexts = vid.iloc[:-window:stride, :].copy()\n",
    "        contexts[\"text\"] = [' '.join(text[start_i:start_i+window])\n",
    "                            for start_i in range(0, len(vid)-window, stride)]\n",
    "        contexts[\"end\"] = [time_end[start_i+window-1]\n",
    "                            for start_i in range(0, len(vid)-window, stride)]        \n",
    "        return contexts\n",
    "    # concat result from all videos\n",
    "    return pd.concat([process_video(vid) for _, vid in raw_df.groupby(\"title\")])\n",
    "\n",
    "df = contextualize(data.to_pandas(), 20, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a71ac56c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48935"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3044e0b0",
   "metadata": {},
   "source": [
    "## Create embedding function\n",
    "We'll call the OpenAI embeddings API to get embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8eefc159",
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "import openai\n",
    "import ratelimiter\n",
    "from retry import retry\n",
    "\n",
    "embed_model = \"text-embedding-ada-002\"\n",
    "\n",
    "# API limit at 60/min == 1/sec\n",
    "limiter = ratelimiter.RateLimiter(max_calls=0.9, period=1.0)\n",
    "\n",
    "# Get the embedding with retry\n",
    "@retry(tries=10, delay=1, max_delay=30, backoff=3, jitter=1)\n",
    "def embed_func(c):    \n",
    "    rs = openai.Embedding.create(input=c, engine=embed_model)\n",
    "    return [record[\"embedding\"] for record in rs[\"data\"]]\n",
    "\n",
    "rate_limited = limiter(embed_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7f5b9a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84194f89598140b2bebbfa55c90cdcc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/49 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "import math\n",
    "\n",
    "openai.api_key = \"sk-...\"\n",
    "\n",
    "# We request in batches rather than 1 embedding at a time\n",
    "def to_batches(arr, batch_size):\n",
    "    length = len(arr)\n",
    "    def _chunker(arr):\n",
    "        for start_i in range(0, len(df), batch_size):\n",
    "            yield arr[start_i:start_i+batch_size]\n",
    "    # add progress meter\n",
    "    yield from tqdm(_chunker(arr), total=math.ceil(length / batch_size))\n",
    "    \n",
    "batch_size = 1000\n",
    "batches = to_batches(df.text.values.tolist(), batch_size)\n",
    "embeds = [emb for c in batches for emb in rate_limited(c)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ea0449",
   "metadata": {},
   "source": [
    "## Create Lance dataset to support vector search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e957b520",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lance\n",
    "import pyarrow as pa\n",
    "from lance.vector import vec_to_table\n",
    "\n",
    "table = vec_to_table(np.array(embeds))\n",
    "combined = pa.Table.from_pandas(df).append_column(\"vector\", table[\"vector\"])\n",
    "ds = lance.write_dataset(combined, \"chatbot.lance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ab509cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building vector index: IVF64,OPQ96, metric=l2\n",
      "Sample 16384 out of 48935 to train kmeans of 1536 dim, 64 clusters\n"
     ]
    }
   ],
   "source": [
    "ds = ds.create_index(\"vector\",\n",
    "                     index_type=\"IVF_PQ\", \n",
    "                     num_partitions=64,  # IVF\n",
    "                     num_sub_vectors=96)  # PQ"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23afc2f9",
   "metadata": {},
   "source": [
    "## Create and answer the prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "06d8b867",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_prompt(query, context):\n",
    "    limit = 3750\n",
    "\n",
    "    prompt_start = (\n",
    "        \"Answer the question based on the context below.\\n\\n\"+\n",
    "        \"Context:\\n\"\n",
    "    )\n",
    "    prompt_end = (\n",
    "        f\"\\n\\nQuestion: {query}\\nAnswer:\"\n",
    "    )\n",
    "    # append contexts until hitting limit\n",
    "    for i in range(1, len(context)):\n",
    "        if len(\"\\n\\n---\\n\\n\".join(context.text[:i])) >= limit:\n",
    "            prompt = (\n",
    "                prompt_start +\n",
    "                \"\\n\\n---\\n\\n\".join(context.text[:i-1]) +\n",
    "                prompt_end\n",
    "            )\n",
    "            break\n",
    "        elif i == len(context)-1:\n",
    "            prompt = (\n",
    "                prompt_start +\n",
    "                \"\\n\\n---\\n\\n\".join(context.text) +\n",
    "                prompt_end\n",
    "            )    \n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e09c5142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The 12th person on the moon was Harrison Schmitt, and he landed on December 11, 1972.'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def complete(prompt):\n",
    "    # query text-davinci-003\n",
    "    res = openai.Completion.create(\n",
    "        engine='text-davinci-003',\n",
    "        prompt=prompt,\n",
    "        temperature=0,\n",
    "        max_tokens=400,\n",
    "        top_p=1,\n",
    "        frequency_penalty=0,\n",
    "        presence_penalty=0,\n",
    "        stop=None\n",
    "    )\n",
    "    return res['choices'][0]['text'].strip()\n",
    "\n",
    "# check that it works\n",
    "query = \"who was the 12th person on the moon and when did they land?\"\n",
    "complete(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "8fcef773",
   "metadata": {},
   "outputs": [],
   "source": [
    "def answer(question):\n",
    "    emb = embed_func(query)[0]\n",
    "    context = ds.to_table(\n",
    "        nearest={\n",
    "            \"column\": \"vector\",\n",
    "            \"k\": 3,\n",
    "            \"q\": emb,\n",
    "            \"nprobes\": 20,\n",
    "            \"refine_factor\": 100\n",
    "        }).to_pandas()\n",
    "    prompt = create_prompt(question, context)\n",
    "    return complete(prompt), context.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "57b5d672",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28705959",
   "metadata": {},
   "source": [
    "## Show the answer and show the video at the right place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "25714299",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLI with multiple negative ranking loss.\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/pNvujJ1XyeQ?start=289.76\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x3998fea40>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = (\"Which training method should I use for sentence transformers \"\n",
    "         \"when I only have pairs of related sentences?\")\n",
    "completion, context = answer(query)\n",
    "\n",
    "print(completion)\n",
    "top_match = context.iloc[0]\n",
    "YouTubeVideo(top_match[\"url\"].split(\"/\")[-1], start=top_match[\"start\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "784d6a65",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
